Clifford Chance and Deutsche Bank looked at the growing intersection of AI and DLT in a new report.

AI predictability and DLT immutability could create autonomous systems that move beyond efficiency to self-verifiability, capable of executing, auditing and learning simultaneously.

AI-augmented smart contracts translate business logic into executable code through LLMs, lowering technical barriers and accelerating deployment. Deutsche Bank’s own pilot with finaXai for tokenized fund servicing illustrates how AI can automate code audits and stress-test logic across multi-jurisdictional workflows.

AI-powered blockchain oracles enhance market-data integrity and anomaly detection, evolving from passive data relays to predictive control layers. The Chainlink–Euroclear–Swift initiative demonstrates how AI-driven oracle networks could synchronise fragmented corporate-action data, cutting validation costs that currently exceed USD 3–5 million annually across custodians.

Tokenized data marketplaces use DLT to enable verifiable AI training through traceable data provenance and federated learning allowing hospitals or banks to train models locally without exposing raw data. This architecture could unlock high-value private datasets while aligning with GDPR, the EU AI Act, and MiCA’s data-governance provisions.

Agentic AI with blockchain wallets gives autonomous systems controlled financial agency. By combining multi-signature wallets with programmable limits, these agents could conduct low-value transactions independently, laying the groundwork for AI-to-AI commerce within a USD 36 trillion digital-payments economy, from automated insurance claims to real-time treasury optimization

Yet convergence amplifies regulatory tension.

Huge breakthrough from Google DeepMind. The study was led by AlphaGo’s creator, David Silver.

In their latest Nature paper, “Discovering SOTA reinforcement learning algorithms,” they show that AI can autonomously discover better RL algorithms.

"Enabling machines to discover learning algorithms for themselves is one of the most promising ideas in AI."

Could the next generation of RL algorithms be machine-discovered?

Google rolled out a Gemini-powered personal health coach inside Fitbit. It uses a deep-agent architecture to orchestrate between conversational, data science, and domain expert sub-agents.

- Performs complex numerical reasoning on physiological time series data.
- Available to eligible U.S. Android Fitbit Premium users, with iOS expanding soon.
- Validated via 1 million+ human annotations and 100k+ hours of evaluation.
- Personalized guidance via a 5-10 minute interactive text or voice conversation.
- Adaptive plans based on individual health metrics and goals.
- Grounded in behavioral science and Consumer Health Advisory Panel.

Western Union to build stablecoin on Solana blockchain, issue with Anchorage

Western Union, the global remittance giant processing around 70 million cross-border transactions quarterly across 200+ countries, has announced plans to launch its own dollar-backed stablecoin in the first half of 2026.

The token, dubbed USDPT (U.S. Dollar Payment Token), will be built on the Solana blockchain. Solana was chosen for its high-speed, low-cost transactions—ideal for remittances—potentially enabling near-instant settlements and slashing fees compared to legacy systems.

Expected in H1 2026, starting as a pilot in select corridors (e.g., South America and Africa) before broader rollout.

Use Cases:
- Faster cross-border transfers with real-time settlement.
- Improved FX pricing in low-liquidity markets.
- Customer custody options, acting like a "savings account" in USD for users in high-inflation regions.

Western Union is also developing a digital wallet network with third-party providers to let users buy, sell, and hold stablecoins directly. CEO Devin McGranahan emphasized stablecoins as an "opportunity, not a threat," positioning the company to compete with rivals like PayPal (PYUSD) and MoneyGram.

OpenAI is planning to build an AI Cloud Platform. "More value created by people building on the platform than by the platform builder".

OpenAI have a clear line of sight to automating AI research. Chief Scientist Jakub Pachocki said that OpenAI expects to be able to develop an automated AI research intern by September 2026 and fully automated AI researcher by March 2028.

Ant group presents the 1st trillion-scale open-source thinking model

Key highlights:

- Superior thinking performance 93.40 on AIME25, 86.72 on HMMT25, 2088 on CodeForces, 55.94 on ARC-AGI-1, plus a silver medal on IMO 2025

- IcePop Mitigates the training-inference mismatch issue in MoE RL, ensuring stable and growing RL training

- C3PO++ Introduces a dynamic rollout partitioning mechanism, achieving 2.5x faster inference and 1.5x faster training
- ASystem a high-performance RL framework designed for efficiently scaling training to trillion-parameter thinking models

Salesforce introduced MMPersuade, a comprehensive multimodal benchmark that assesses AI agents’ susceptibility to established persuasion principles, covering commercial, subjective and behavioral, and adversarial contexts.

MMPersuade is a new dataset and evaluation framework to systematically study multimodal persuasion in LVLMs.

Team built a comprehensive multimodal benchmark pairing persuasive strategies with over 62,000 images and 4,700 videos.

It covers 3 key contexts: Commercial (Sales & Ads), Subjective & Behavioral (Health Nudging, Politics) , Adversarial (Misinformation & Fabricated Claims)

Carnegie, Stanford introduced a new work on Training LLMs to Discover Abstractions for Solving Reasoning Problems

MIT presented LoRA vs full fine-tuning: same performance ≠ same solution.

This paper shows that LoRA and full fine-tuning, even when equally well fit, learn structurally different solutions and that LoRA forgets less and can be made even better (lesser forgetting) by a simple intervention.

New Anthropic research: Signs of introspection in LLMs.

Can language models recognize their own internal thoughts? Or do they just make up plausible answers when asked about them?

Anthropic found evidence for genuine—though limited—introspective capabilities in Claude.

Researchers developed a method to distinguish true introspection from made-up answers: inject known concepts into a model's “brain,” then see how these injections affect the model’s self-reported internal states.

In one experiment, researchers asked the model to detect when a concept is injected into its “thoughts.” When researchers inject a neural pattern representing a particular concept, Claude can in some cases detect the injection, and identify the concept.

However, it doesn’t always work. In fact, most of the time, models fail to exhibit awareness of injected concepts, even when they are clearly influenced by the injection.

Also show that Claude introspects in order to detect artificially prefilled outputs. Normally, Claude apologizes for such outputs. But if researchers retroactively inject a matching concept into its prior activations, team can fool Claude into thinking the output was intentional.

This reveals a mechanism that checks consistency between intention and execution. The model appears to compare "what did I plan to say?" against "what actually came out?"—a form of introspective monitoring happening in natural circumstances.

Also found evidence for cognitive control, where models deliberately "think about" something. For instance, when team instruct a model to think about "aquariums” in an unrelated context, researchers measure higher aquarium-related neural activity than if team instruct it not to.

Note that experiments do not address the question of whether AI models can have subjective experience or human-like self-awareness. The mechanisms underlying the behaviors observe are unclear, and may not have the same philosophical significance as human introspection.

While currently limited, AI models’ introspective capabilities will likely grow more sophisticated. Introspective self-reports could help improve the transparency of AI models’ decision-making—but should not be blindly trusted.

Cognition (ex-team of Windsurf) released SWE-1.5, fast agent model.

That delivers "near-SOTA coding performance" at significantly higher speeds.

U can try it here.

Perplexity launched Perplexity Patents, a new IP intelligence research Agent

"While in beta, Perplexity Patents will be free for all users. Pro and Max subscribers will receive additional usage quotas and model configuration options."

OpenAI introduced Aardvark, an agent that finds and fixes security bugs using GPT-5.

Microsoft announced new agents + economics research

AI agents are starting to shop and buy for us. At the same time, agents are representing and providing customer support on behalf of businesses.

Real markets are messy: hundreds of options, agents with hidden strategies, conversations that can go anywhere. Microsoft built a simulated marketplace to test this at scale - and found issues that need fixing.

Approach: create a safe testing ground where AI shoppers and AI sellers can interact exactly like they would in the real world - searching, haggling, paying. And systematically test what goes wrong.

It's open source, so anyone building these systems can test before launching. Think of it like a flight simulator, but for AI commerce.

Key findings: The best AI models can find near-optimal deals - but only when search is perfect. Add real-world messiness and performance tanks. Worse: ALL models (even the best) grab the first decent offer, creating a 10-30x advantage for speed over quality.

More options paradoxically made results worse. Some models fell for fake credentials and manipulation.

The future: We need agents that truly compare options, markets that work at massive scale, and market designs that stay fair when humans and AI trade together. This simulator gives us a safe place to figure that out before real money is at stake.

DeepAnalyze: Agentic LLM for Autonomous Data Science

DeepAnalyze-8B is the first agentic LLM capable of handling the entire data science pipeline—from raw data to analyst-grade research reports—without predefined workflows.

It learns like a human via a curriculum-based agentic training paradigm and a data-grounded trajectory synthesis process.

Despite having just 8B parameters, DeepAnalyze surpasses workflow-based agents built on proprietary LLMs, marking a major step toward open, autonomous data science.

GitHub.

The first research on the fundamentals of character training i.e. applying modern post training techniques to ingrain specific character traits into models.

Researchers used Constitutional AI + a new synthetic data pipeline:

1. Distillation (DPO from teacher embodying the constitution)
2. Introspection (the model generates its own character traits beyond the constitution)

Result: 11 different personas each trained on Llama 3.1, Qwen 2.5, and Gemma 3. All model weights are available.

A new eval measures the traits models choose to express on their own (revealed preferences).
Traits chosen more often have higher Elo scores. The difference before and after character training reveals its effect.

All models, datasets, code released.

New work from Google DeepMind on benchmarks and autograders they used on IMO Gold journey.

Main takeouts are:
- autograding can achieve ~90% accuracy even on long and difficult reasoning
- DeepThink is quite behind IMO Gold model on very difficult problems

Future House introduced Kosmos, an AI scientist system for data-driven discovery

Kosmos is a multi-agent system designed around a central “world model” to coordinate information across hundreds of scientific agent instances.

Use it.

Given an open-ended objective and dataset, Kosmos can perform up to 12 hours of research to explore, analyze, and complete the objective.

Team presented 7 expert-validated discoveries that Kosmos generated or reproduced across scientific disciplines, including:

1. A novel mechanism of ENT neuron vulnerability with aging
2. Identifying a critical determinant for perovskite performance
3. Evidence that high SOD2 levels may causally reduce myocardial fibrosis.

TSMC broke ground on the world’s most advanced 1.4nm semiconductor fab, a total NT$1.5 trillion (US$48.5 billion) investment in the central Taiwan city of Taichung.

Mass production will start in 2028, with annual revenue seen at NT$500 billion ($16.2 billion).

GPT-5.1 confirmed as new traces of "gpt-5-1-thinking" have been spotted on ChatGPT.

Can AI invent new math? A new paper from Google DeepMind and renowned mathematician Terence Tao shows how.

Using AlphaEvolve, the team merges LLM-generated ideas with automated evaluation to propose, test, and refine mathematical algorithms.

In tests on 67 problems across analysis, geometry, and number theory, AlphaEvolve not only rediscovered known results but often improved upon them—even generalizing finite cases into universal formulas.

Paired with DeepThink and AlphaProof, it points toward a future where AI doesn’t just assist mathematicians—it collaborates with them in discovery.